Principal component score modeling for the rapid description of chromatographic separations

This paper describes the use of Principal Component Analysis (PCA) as a tool for modeling chromatographic separations. PCA is an analytical technique developed to extract key information out of large data sets and to develop relationships and correlations. The basis of the proposed model is the use of PCA to correlate experimental chromatographic data across different process variables or scales. The generated correlations are then used to provide for the simulation of additional chromatographic runs not included in the initial dataset. The approach is demonstrated by application to the cation exchange separation of a four protein component feed comprising ovalbumin, ovatransferrin, lysozyme, and myoglobin. A good fit between modeled and experimental data was found, and the ability of the method to model additional chromatographic separations not within the original dataset is demonstrated. The technique has the potential to accommodate changing system variables such as column dimensions as well as process variables including sample volume and salt gradient. It provides a potentially powerful tool for the rapid investigation of scale-up effects and for the minimization of the material inventories needed for such studies.